{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "——————————————————————————————数据概览—————————————————————————————————",
   "id": "347f8bb4864c04d4"
  },
  {
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-05-16T04:16:16.442696Z",
     "start_time": "2025-05-16T04:16:12.501555Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "\n",
    "#导入ucimlrepo\n",
    "from ucimlrepo import fetch_ucirepo\n",
    "\n",
    "\n",
    "# 获取数据集\n",
    "phishing_websites = fetch_ucirepo(id=327)\n",
    "\n",
    "# 数据特征与标签\n",
    "X = phishing_websites.data.features \n",
    "y = phishing_websites.data.targets\n",
    "\n",
    "\n",
    "# 创建特征DataFrame（添加列名）\n",
    "features_df = pd.DataFrame(phishing_websites.data.features,\n",
    "                          columns=phishing_websites.data.features_names)\n",
    "\n",
    "# 创建目标变量DataFrame（列名设置为'Target'）\n",
    "target_df = pd.DataFrame(phishing_websites.data.targets, \n",
    "                        columns=['Target'])\n",
    "\n",
    "# 横向合并特征和目标（axis=1表示列方向合并）\n",
    "full_df = pd.concat([features_df, target_df], axis=1)\n",
    "\n",
    "# 显示数据前5行（带列名）\n",
    "print(\"——————————————————————数据集前五行——————————————————————————\")\n",
    "print(full_df.head())\n",
    "\n",
    "# 数据集概览函数\n",
    "def print_dataset_summary(X, y):\n",
    "    # 样本规模\n",
    "    total_samples = X.shape[0]\n",
    "    phishing_count = sum(y.iloc[:, 0] == 1)  \n",
    "    legitimate_count = total_samples - phishing_count\n",
    "    ratio = legitimate_count / phishing_count\n",
    "\n",
    "    # 特征构成\n",
    "    num_features = X.shape[1]\n",
    "\n",
    "    # 打印输出\n",
    "    print(\"————————————————————数据集概览————————————————————————————\")\n",
    "    print(f\"• 样本规模：{total_samples} 条（钓鱼网站 {phishing_count} 条，合法网站 {legitimate_count} 条，比例 1:{ratio:.2f}）\")\n",
    "    print(f\"• 特征构成：{num_features} 个数值型/分类型特征 + 1 个二元标签\")\n",
    "\n",
    "# 查看数据集概览\n",
    "print_dataset_summary(X, y)\n"
   ],
   "id": "initial_id",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "——————————————————————数据集前五行——————————————————————————\n",
      "   having_ip_address  url_length  shortining_service  having_at_symbol  \\\n",
      "0                 -1           1                   1                 1   \n",
      "1                  1           1                   1                 1   \n",
      "2                  1           0                   1                 1   \n",
      "3                  1           0                   1                 1   \n",
      "4                  1           0                  -1                 1   \n",
      "\n",
      "   double_slash_redirecting  prefix_suffix  having_sub_domain  sslfinal_state  \\\n",
      "0                        -1             -1                 -1              -1   \n",
      "1                         1             -1                  0               1   \n",
      "2                         1             -1                 -1              -1   \n",
      "3                         1             -1                 -1              -1   \n",
      "4                         1             -1                  1               1   \n",
      "\n",
      "   domain_registration_length  favicon  ...  popupwindow  iframe  \\\n",
      "0                          -1        1  ...            1       1   \n",
      "1                          -1        1  ...            1       1   \n",
      "2                          -1        1  ...            1       1   \n",
      "3                           1        1  ...            1       1   \n",
      "4                          -1        1  ...           -1       1   \n",
      "\n",
      "   age_of_domain  dnsrecord  web_traffic  page_rank  google_index  \\\n",
      "0             -1         -1           -1         -1             1   \n",
      "1             -1         -1            0         -1             1   \n",
      "2              1         -1            1         -1             1   \n",
      "3             -1         -1            1         -1             1   \n",
      "4             -1         -1            0         -1             1   \n",
      "\n",
      "   links_pointing_to_page  statistical_report  Target  \n",
      "0                       1                  -1     NaN  \n",
      "1                       1                   1     NaN  \n",
      "2                       0                  -1     NaN  \n",
      "3                      -1                   1     NaN  \n",
      "4                       1                   1     NaN  \n",
      "\n",
      "[5 rows x 31 columns]\n",
      "————————————————————数据集概览————————————————————————————\n",
      "• 样本规模：11055 条（钓鱼网站 6157 条，合法网站 4898 条，比例 1:0.80）\n",
      "• 特征构成：30 个数值型/分类型特征 + 1 个二元标签\n"
     ]
    }
   ],
   "execution_count": 26
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "——————————————————————————————正负样本比例图—————————————————————————————————",
   "id": "9e3616522134c296"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-16T04:16:16.689498Z",
     "start_time": "2025-05-16T04:16:16.514660Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams[\"font.sans-serif\"] = [\"SimHei\"]  # Windows系统\n",
    "plt.rcParams[\"axes.unicode_minus\"] = False\n",
    "\n",
    "# 转换目标变量为Series方便操作\n",
    "y_series = y.iloc[:, 0]  \n",
    "\n",
    "# 创建排除0的掩码\n",
    "mask = y_series != 0\n",
    "\n",
    "# 过滤数据集\n",
    "y_filtered = y_series[mask]\n",
    "class_counts = y_filtered.value_counts()\n",
    "\n",
    "# 定义标签映射\n",
    "label_map = {\n",
    "    -1: \"Phishing (负样本)\",\n",
    "    1: \"Legitimate (正样本)\"\n",
    "}\n",
    "\n",
    "# 创建饼图\n",
    "plt.figure(figsize=(8, 6))\n",
    "patches, texts, autotexts = plt.pie(\n",
    "    class_counts,\n",
    "    labels=[label_map[x] for x in class_counts.index],\n",
    "    autopct='%1.1f%%',\n",
    "    colors=['#ff9999', '#66b3ff'],\n",
    "    startangle=90,\n",
    "    explode=(0.05, 0.05)\n",
    ")\n",
    "\n",
    "\n",
    "plt.axis('equal')  # 保持圆形\n",
    "\n",
    "# 添加图例\n",
    "plt.legend(\n",
    "    title=\"类别说明\",\n",
    "    loc=\"upper left\",\n",
    "    bbox_to_anchor=(0.9, 0.9),\n",
    "    labels=[f\"{label_map[k]} ({v}个)\" for k, v in class_counts.items()]\n",
    ")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "id": "ed8899a23eb57722",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 27
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "——————————————————————————————数据预处理—————————————————————————————————",
   "id": "8173c9ba01b5d00f"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-16T04:16:30.753620Z",
     "start_time": "2025-05-16T04:16:16.772842Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 导入库\n",
    "from ucimlrepo import fetch_ucirepo\n",
    "import pandas as pd\n",
    "\n",
    "# 获取数据集\n",
    "phishing_websites = fetch_ucirepo(id=327)\n",
    "X = phishing_websites.data.features\n",
    "y = phishing_websites.data.targets\n",
    "\n",
    "# 转换为DataFrame便于操作\n",
    "X = pd.DataFrame(X)\n",
    "y = pd.Series(y.iloc[:, 0])  # 目标列为单列\n",
    "\n",
    "# 1. 验证缺失值（官方已声明无缺失）\n",
    "print(\"缺失值统计：\")\n",
    "print(X.isnull().sum())\n",
    "\n",
    "# 2. 查看特征取值范围（验证数值范围一致性）\n",
    "print(\"\\n特征值范围：\")\n",
    "for col in X.columns:\n",
    "    print(f\"{col}: {sorted(X[col].unique())}\")\n",
    "\n",
    "# 3. 确认标签分布\n",
    "print(\"\\n标签分布：\")\n",
    "print(y.value_counts())"
   ],
   "id": "abd6deaca2039395",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "缺失值统计：\n",
      "having_ip_address             0\n",
      "url_length                    0\n",
      "shortining_service            0\n",
      "having_at_symbol              0\n",
      "double_slash_redirecting      0\n",
      "prefix_suffix                 0\n",
      "having_sub_domain             0\n",
      "sslfinal_state                0\n",
      "domain_registration_length    0\n",
      "favicon                       0\n",
      "port                          0\n",
      "https_token                   0\n",
      "request_url                   0\n",
      "url_of_anchor                 0\n",
      "links_in_tags                 0\n",
      "sfh                           0\n",
      "submitting_to_email           0\n",
      "abnormal_url                  0\n",
      "redirect                      0\n",
      "on_mouseover                  0\n",
      "rightclick                    0\n",
      "popupwindow                   0\n",
      "iframe                        0\n",
      "age_of_domain                 0\n",
      "dnsrecord                     0\n",
      "web_traffic                   0\n",
      "page_rank                     0\n",
      "google_index                  0\n",
      "links_pointing_to_page        0\n",
      "statistical_report            0\n",
      "dtype: int64\n",
      "\n",
      "特征值范围：\n",
      "having_ip_address: [-1, 1]\n",
      "url_length: [-1, 0, 1]\n",
      "shortining_service: [-1, 1]\n",
      "having_at_symbol: [-1, 1]\n",
      "double_slash_redirecting: [-1, 1]\n",
      "prefix_suffix: [-1, 1]\n",
      "having_sub_domain: [-1, 0, 1]\n",
      "sslfinal_state: [-1, 0, 1]\n",
      "domain_registration_length: [-1, 1]\n",
      "favicon: [-1, 1]\n",
      "port: [-1, 1]\n",
      "https_token: [-1, 1]\n",
      "request_url: [-1, 1]\n",
      "url_of_anchor: [-1, 0, 1]\n",
      "links_in_tags: [-1, 0, 1]\n",
      "sfh: [-1, 0, 1]\n",
      "submitting_to_email: [-1, 1]\n",
      "abnormal_url: [-1, 1]\n",
      "redirect: [0, 1]\n",
      "on_mouseover: [-1, 1]\n",
      "rightclick: [-1, 1]\n",
      "popupwindow: [-1, 1]\n",
      "iframe: [-1, 1]\n",
      "age_of_domain: [-1, 1]\n",
      "dnsrecord: [-1, 1]\n",
      "web_traffic: [-1, 0, 1]\n",
      "page_rank: [-1, 1]\n",
      "google_index: [-1, 1]\n",
      "links_pointing_to_page: [-1, 0, 1]\n",
      "statistical_report: [-1, 1]\n",
      "\n",
      "标签分布：\n",
      " 1    6157\n",
      "-1    4898\n",
      "Name: result, dtype: int64\n"
     ]
    }
   ],
   "execution_count": 28
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "——————————————————————————————特征工程—————————————————————————————————",
   "id": "2b4d464f4bd9bcf2"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-16T04:16:30.908329Z",
     "start_time": "2025-05-16T04:16:30.833326Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "from sklearn.feature_selection import VarianceThreshold, SelectKBest,f_classif\n",
    "# 将目标变量转换为numpy数组\n",
    "y_num = y.values.ravel()\n",
    "\n",
    "# 一、方差过滤法（去除零方差特征）\n",
    "# --------------------------------------------------------\n",
    "# 初始化方差阈值选择器，设置threshold=0过滤零方差特征\n",
    "var_selector = VarianceThreshold(threshold=0)\n",
    "X_var = var_selector.fit_transform(X)\n",
    "\n",
    "# 获取被保留的特征名称\n",
    "selected_mask = var_selector.get_support()\n",
    "selected_features = X.columns[selected_mask].tolist()\n",
    "print(f\"方差过滤后保留特征数：{len(selected_features)}个\")\n",
    "\n",
    "\n",
    "# 二、F检验筛选（选择与目标相关性最强的特征）\n",
    "# --------------------------------------------------------\n",
    "# SelectKBest是特征选择方法，用于选择得分最高的K个特征\n",
    "# f_classif是F检验的分类函数，用于计算特征与目标变量之间的F值\n",
    "# k=10表示我们只保留最重要的10个特征\n",
    "f_selector = SelectKBest(score_func=f_classif, k=15)\n",
    "# 拟合数据并进行特征转换，X_f是筛选后的特征矩阵\n",
    "X_f = f_selector.fit_transform(X_var, y)\n",
    "\n",
    "# 获取特征F值及对应名称\n",
    "# f_selector.scores_包含每个特征的F值评分\n",
    "f_scores = f_selector.scores_\n",
    "\n",
    "selected_indices = np.argsort(f_scores)[-10:][::-1]\n",
    "# 根据索引获取对应的特征名称\n",
    "top10_features = [selected_features[i] for i in selected_indices]\n",
    "\n",
    "# 创建特征分析表格\n",
    "# 使用DataFrame存储特征名称、方差和F值信息\n",
    "feature_analysis = pd.DataFrame({\n",
    "    'Feature': selected_features,           # 特征名称\n",
    "    # 计算每个特征的方差，反映数据的离散程度\n",
    "    'Variance': X[selected_features].var().values,  \n",
    "    'F_Score': f_scores                     # F检验得分\n",
    "})\n",
    "# 按F_Score降序排序，并选取前10行\n",
    "feature_analysis = feature_analysis.sort_values(by='F_Score', ascending=False).head(15)\n",
    "# 结果展示\n",
    "print(\"\\nF检验最具代表性的15个特征：\")\n",
    "# 显示筛选后的特征及其方差和F值\n",
    "display(feature_analysis[['Feature', 'Variance', 'F_Score']])\n",
    "\n",
    "\n",
    "# 三、数据集划分\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 数据集划分（分层抽样保持类别比例）\n",
    "# 训练集：用于模型学习参数\n",
    "# 测试集：用于评估模型的泛化能力\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X,  # 标准化后的特征矩阵\n",
    "    y,  # 目标变量\n",
    "    test_size=0.3,  # 测试集占比30%\n",
    "    stratify=y,  # 分层抽样：确保训练集和测试集的类别比例与原始数据一致\n",
    "    random_state=42  # 固定随机种子，保证结果可复现\n",
    ")\n",
    "\n",
    "# 输出数据集形状，验证划分结果\n",
    "print(\"▍训练集形状:\", X_train.shape) \n",
    "print(\"▍测试集形状:\", X_test.shape)  "
   ],
   "id": "65fd688047bd853",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "方差过滤后保留特征数：30个\n",
      "\n",
      "F检验最具代表性的15个特征：\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "                       Feature  Variance       F_Score\n",
       "7               sslfinal_state  0.831548  11543.570464\n",
       "13               url_of_anchor  0.511422  10209.239894\n",
       "5                prefix_suffix  0.459873   1529.043445\n",
       "25                 web_traffic  0.685142   1504.193018\n",
       "6            having_sub_domain  0.668336   1079.778531\n",
       "12                 request_url  0.965196    758.253010\n",
       "14               links_in_tags  0.583654    725.777602\n",
       "8   domain_registration_length  0.886666    593.762202\n",
       "15                         sfh  0.576298    569.825002\n",
       "27                google_index  0.479374    186.899510\n",
       "23               age_of_domain  0.996340    165.602039\n",
       "26                   page_rank  0.766130    122.376589\n",
       "0            having_ip_address  0.901614     98.873891\n",
       "29          statistical_report  0.482243     70.938416\n",
       "24                   dnsrecord  0.857862     63.734211"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>Variance</th>\n",
       "      <th>F_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sslfinal_state</td>\n",
       "      <td>0.831548</td>\n",
       "      <td>11543.570464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>url_of_anchor</td>\n",
       "      <td>0.511422</td>\n",
       "      <td>10209.239894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>prefix_suffix</td>\n",
       "      <td>0.459873</td>\n",
       "      <td>1529.043445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>web_traffic</td>\n",
       "      <td>0.685142</td>\n",
       "      <td>1504.193018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>having_sub_domain</td>\n",
       "      <td>0.668336</td>\n",
       "      <td>1079.778531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>request_url</td>\n",
       "      <td>0.965196</td>\n",
       "      <td>758.253010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>links_in_tags</td>\n",
       "      <td>0.583654</td>\n",
       "      <td>725.777602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>domain_registration_length</td>\n",
       "      <td>0.886666</td>\n",
       "      <td>593.762202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>sfh</td>\n",
       "      <td>0.576298</td>\n",
       "      <td>569.825002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>google_index</td>\n",
       "      <td>0.479374</td>\n",
       "      <td>186.899510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>age_of_domain</td>\n",
       "      <td>0.996340</td>\n",
       "      <td>165.602039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>page_rank</td>\n",
       "      <td>0.766130</td>\n",
       "      <td>122.376589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>having_ip_address</td>\n",
       "      <td>0.901614</td>\n",
       "      <td>98.873891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>statistical_report</td>\n",
       "      <td>0.482243</td>\n",
       "      <td>70.938416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>dnsrecord</td>\n",
       "      <td>0.857862</td>\n",
       "      <td>63.734211</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "▍训练集形状: (7738, 30)\n",
      "▍测试集形状: (3317, 30)\n"
     ]
    }
   ],
   "execution_count": 29
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "——————————————————————————————模型训练—————————————————————————————————",
   "id": "46ff129ca091d2de"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-16T04:16:31.609116Z",
     "start_time": "2025-05-16T04:16:31.083228Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 导入库\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "\n",
    "# 初始化模型\n",
    "rf_model = RandomForestClassifier(n_estimators=100,max_depth=5, random_state=42)\n",
    "\n",
    "# 训练模型\n",
    "rf_model.fit(X_train, y_train)\n",
    "\n",
    "# 预测并评估\n",
    "y_pred = rf_model.predict(X_test)\n",
    "print(f\"准确率: {accuracy_score(y_test, y_pred):.4f}\")\n",
    "print(classification_report(y_test, y_pred))"
   ],
   "id": "f7296484cf097842",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "准确率: 0.9313\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       0.94      0.90      0.92      1470\n",
      "           1       0.92      0.96      0.94      1847\n",
      "\n",
      "    accuracy                           0.93      3317\n",
      "   macro avg       0.93      0.93      0.93      3317\n",
      "weighted avg       0.93      0.93      0.93      3317\n",
      "\n"
     ]
    }
   ],
   "execution_count": 30
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "——————————————————————————————模型优化—————————————————————————————————",
   "id": "faf4d47eb2c43adc"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-16T04:16:52.725418Z",
     "start_time": "2025-05-16T04:16:31.641107Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "# 一、重要特征\n",
    "'''\n",
    "参数说明：\n",
    "- n_estimators=100：使用100棵决策树\n",
    "- oob_score=True：启用袋外评估\n",
    "- random_state=42：固定随机种子保证结果可复现\n",
    "'''\n",
    "rf = RandomForestClassifier(n_estimators=100,\n",
    "                            oob_score=True,\n",
    "                            random_state=42)\n",
    "\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "# 获取特征重要性得分（基于平均不纯度减少）\n",
    "importances = rf.feature_importances_\n",
    "\n",
    "# 将特征名称与重要性得分组合\n",
    "features = X.columns\n",
    "indices = np.argsort(importances)[::-1]  # 按重要性降序排列的索引\n",
    "\n",
    "# 打印前20个重要特征\n",
    "print(\"\\nTop 20重要特征：\")\n",
    "for i in range(20):\n",
    "    print(f\"{i + 1}. {features[indices[i]]}: {importances[indices[i]]:.4f}\")\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.title(\"特征重要性排名（Top 20）\")\n",
    "plt.bar(range(20), importances[indices][:20], align='center')\n",
    "plt.xticks(range(20), features[indices][:20], rotation=90)\n",
    "plt.xlabel(\"特征名称\")\n",
    "plt.ylabel(\"重要性得分\")\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "# 二、袋外评估\n",
    "# 使用袋外数据评估\n",
    "rfc_oob = RandomForestClassifier(n_estimators=100, oob_score=True, random_state=42)\n",
    "rfc_oob.fit(X, y)\n",
    "print(f\"袋外准确率: {rfc_oob.oob_score_:.4f}\")\n",
    "\n",
    "\n",
    "\n",
    "# 三、网格搜索\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# 创建基础模型\n",
    "base_model = RandomForestClassifier(random_state=42)\n",
    "\n",
    "# 设置参数网格（这里展示常用参数，可根据实际情况调整）\n",
    "param_grid = {\n",
    "    'n_estimators': [100, 200, 300],      # 树的数量\n",
    "    'max_depth': [1, 10],         # 树的最大深度\n",
    "    'min_samples_split': [10, 15],         # 节点分裂最小样本数\n",
    "}\n",
    "\n",
    "# 创建网格搜索对象\n",
    "grid_search = GridSearchCV(\n",
    "    estimator=base_model,\n",
    "    param_grid=param_grid,\n",
    "    cv=5,               # 5折交叉验证\n",
    "    n_jobs=-1,          # 使用所有CPU核心\n",
    "    verbose=2,          # 显示详细过程\n",
    "    scoring='accuracy'  # 使用准确率评估\n",
    ")\n",
    "\n",
    "# 执行网格搜索（仅使用训练集）\n",
    "grid_search.fit(X_train, y_train.values.ravel())\n",
    "\n",
    "# 输出最佳参数和得分\n",
    "print(\"最佳参数:\", grid_search.best_params_)\n",
    "print(\"训练集最佳准确率:\", grid_search.best_score_)\n",
    "\n",
    "\n",
    "\n",
    "# 四、学习曲线绘制\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import learning_curve\n",
    "\n",
    "# 使用找到的最佳参数创建最终模型\n",
    "best_model = grid_search.best_estimator_\n",
    "\n",
    "# 设置学习曲线参数\n",
    "train_sizes = np.linspace(0.1, 1.0, 5)  # 训练集比例从10%到100%分5个阶段，[0.1, 0.3, 0.5, 0.75, 1.0]\n",
    "\n",
    "# 计算学习曲线数据\n",
    "train_sizes, train_scores, val_scores = learning_curve(\n",
    "    estimator=best_model,\n",
    "    X=X_train,\n",
    "    y=y_train.values.ravel(),\n",
    "    train_sizes=train_sizes,\n",
    "    cv=5,                # 5折交叉验证\n",
    "    n_jobs=-1,           # 并行计算\n",
    "    scoring='accuracy',  # 使用准确率评估\n",
    "    random_state=42      # 保证可重复性\n",
    ")\n",
    "\n",
    "# 计算平均值和标准差\n",
    "train_mean = np.mean(train_scores, axis=1)\n",
    "train_std = np.std(train_scores, axis=1)\n",
    "val_mean = np.mean(val_scores, axis=1)\n",
    "val_std = np.std(val_scores, axis=1)\n",
    "\n",
    "# 绘制学习曲线\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(train_sizes, train_mean, 'o-', color='r', label='Training score')\n",
    "plt.plot(train_sizes, val_mean, 'o-', color='g', label='Cross-validation score')\n",
    "plt.fill_between(train_sizes, train_mean - train_std, train_mean + train_std, alpha=0.1, color='r')\n",
    "plt.fill_between(train_sizes, val_mean - val_std, val_mean + val_std, alpha=0.1, color='g')\n",
    "\n",
    "plt.title('Learning Curve')\n",
    "plt.xlabel('Training examples')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(loc='best')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ],
   "id": "3d9b6f1cce857358",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Top 20重要特征：\n",
      "1. sslfinal_state: 0.3222\n",
      "2. url_of_anchor: 0.2555\n",
      "3. web_traffic: 0.0740\n",
      "4. having_sub_domain: 0.0657\n",
      "5. links_in_tags: 0.0410\n",
      "6. prefix_suffix: 0.0342\n",
      "7. sfh: 0.0190\n",
      "8. links_pointing_to_page: 0.0187\n",
      "9. request_url: 0.0184\n",
      "10. domain_registration_length: 0.0168\n",
      "11. age_of_domain: 0.0156\n",
      "12. google_index: 0.0136\n",
      "13. having_ip_address: 0.0134\n",
      "14. dnsrecord: 0.0133\n",
      "15. page_rank: 0.0116\n",
      "16. url_length: 0.0081\n",
      "17. https_token: 0.0063\n",
      "18. redirect: 0.0058\n",
      "19. shortining_service: 0.0055\n",
      "20. submitting_to_email: 0.0054\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "袋外准确率: 0.9731\n",
      "Fitting 5 folds for each of 12 candidates, totalling 60 fits\n",
      "最佳参数: {'max_depth': 10, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "训练集最佳准确率: 0.9479190364279283\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 31
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "——————————————————————————————模型评估—————————————————————————————————",
   "id": "d06e93a0ea1e5858"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-16T04:16:54.208909Z",
     "start_time": "2025-05-16T04:16:52.820353Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 一、分类报告\n",
    "# 导入库\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "\n",
    "rf_model = RandomForestClassifier(**grid_search.best_params_)\n",
    "\n",
    "# 训练模型\n",
    "rf_model.fit(X_train, y_train)\n",
    "\n",
    "# 预测并评估\n",
    "y_pred = rf_model.predict(X_test)\n",
    "print(f\"准确率: {accuracy_score(y_test, y_pred):.4f}\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "\n",
    "\n",
    "# 二、混淆矩阵\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred) \n",
    "\n",
    "# 打印数值版混淆矩阵\n",
    "print(\"\\n混淆矩阵数值：\") \n",
    "print(cm)\n",
    "\n",
    "# 可视化版（带标签的热力图）\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, \n",
    "                              display_labels=[\"正常网站\", \"钓鱼网站\"])\n",
    "disp.plot(cmap=\"Blues\", values_format=\"d\")  # values_format=\"d\"显示整数\n",
    "plt.title(\"网站分类混淆矩阵\")\n",
    "plt.show()"
   ],
   "id": "be09e32a83e1bf66",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "准确率: 0.9509\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       0.96      0.93      0.94      1470\n",
      "           1       0.94      0.97      0.96      1847\n",
      "\n",
      "    accuracy                           0.95      3317\n",
      "   macro avg       0.95      0.95      0.95      3317\n",
      "weighted avg       0.95      0.95      0.95      3317\n",
      "\n",
      "\n",
      "混淆矩阵数值：\n",
      "[[1364  106]\n",
      " [  57 1790]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 32
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
